THE GROUP DEVELOPMENT PROCESS SEEN THROUGH THE LENS OF COMPLEXITY THEORY 1
Bibliographic record
Abstract
Complexity theory is used to develop a model of group development based on earlier research with social groups such as Tuckman and Jensen’s stages of forming, storming, norming, performing and adjourning (1977). The model shows how these stages correspond to Wolfram’s four attractor classes of order (point and periodic), chaos (strange) and complexity, and transitions among them. Kauffman’s NK models suggests several ways of moving networks among the attractor regions (1995). The theory was tested on data derived from observations by a Facilitator, and feedback from participants, who participated in a series of training workshops for other facilitators, and for members of a labour union. The results confirmed that these training groups made three clearly identifiable transitions among the regions of complexity theory. Initially, the Facilitator breaks down expectations of order, while establishing a multitude of interactions among group members to help the group make the transition from order into the realm of chaos. The result is great uncertainty and unpredictability for both the participants and the Facilitator. Later, through the establishment of goals and simple rules, the group begins to focus, uncertainty decreases, and the group makes a second transition from chaos to complexity, just within the region of order, at the ‘edge of chaos’. Complex groups resemble sports teams in the Zone during peak experiences. They are very creative and
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.040 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".